Driving analysis using vehicle-to-vehicle communication

ABSTRACT

One or more driving analysis computing devices in a driving analysis system may be configured to analyze driving data, determine driving behaviors, and calculate driver scores based on driving data transmitted using vehicle-to-vehicle (V2V) communications. Driving data from multiple vehicles may be collected by vehicle sensors or other vehicle-based systems, transmitted using V2V communications, and then analyzed and compared to determine various driving behaviors by the drivers of the vehicles. Driver scores may be calculated or adjusted based on the determined driving behaviors of vehicle drivers, and also may be calculated or adjusted based on other the driver scores of nearby vehicles.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to co-pendingU.S. application Ser. No. 14/832,197, filed Aug. 21, 2015, which is acontinuation of U.S. application Ser. No. 13/904,682, filed May 29,2013, issued as U.S. Pat. No. 9,147,353 on Sep. 29, 2015 and entitled“Driving Analysis Using Vehicle-to-Vehicle Communication,” which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

Aspects of the disclosure generally relate to the analysis of vehicledriving data. In particular, various aspects of the disclosure relate toreceiving and transmitting driving data using vehicle-to-vehicle (V2V)communications, analyzing driving data, determining driving behaviors

BACKGROUND

Many vehicles include sophisticated sensors and advanced internalcomputer systems designed to monitor and control vehicle operations anddriving functions. Advanced vehicles systems can perform such tasks asmonitoring fuel consumption and optimizing engine operation to achievehigher fuel efficiency, detecting and correcting a loss of traction onan icy road, and detecting a collision and automatically contactingemergency services. Various vehicle-based communication systems allowvehicles to communicate with other devices inside or outside of thevehicle. For example, a Bluetooth system may enable communicationbetween the vehicle and the driver's mobile phone. Telematics systems,such as on-board diagnostics (OBD) systems installed within vehicles,may be configured to access vehicle computers and sensor data andtransmit the data to a display within the vehicle, a personal computeror mobile device, or to a centralized data processing system. Dataobtained from OBD systems has been used for a variety of purposes,including maintenance, diagnosis, and analysis. Additionally,vehicle-to-vehicle (V2V) communication systems can be used to providedrivers with safety warnings and collision alerts based on data receivedfrom other nearby vehicles.

When out on the road, vehicles and drivers may engage in many differenttypes of driving behaviors, including various “social interactions” withother vehicles and drivers. Some social interactions, such as propersignaling and yielding to other vehicles, characterize safe and prudentdriving, while other behaviors, such as tailgating and racing mayrepresent high-risk and unsafe driving.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to methods, computer-readable media,and apparatuses for receiving and transmitting driving data usingvehicle-to-vehicle (V2V) communications, analyzing driving data,determining driving behaviors of vehicles, and calculating driver scoresbased on the determined driving behaviors. One or more computing deviceswithin a vehicle, such as driving analysis module or a user's mobiledevice, or an external computer system, may receive vehicle driving datafrom multiple vehicles nearby one another. Vehicle driving data may becollected by vehicle sensors or other vehicle-based systems, and may betransmitted using one or more V2V communication techniques. Vehicledriving data from multiple vehicles may be analyzed and compared todetermine various driving behaviors of the vehicles' drivers. Forexample, negative driving behaviors such as tailgating, cutting-off,brake-checking, preventing another vehicle from merging, or racing, andpositive driving behaviors such as proper signaling, yielding, defensiveavoidance, or allowing another vehicle to merge, may be determined byanalyzing the vehicles' speeds, relative positions, distances between,and other available sensor data from one or more of the vehicles.

In accordance with further aspects of the present disclosure, driverscores may be calculated or adjusted based on the determined drivingbehaviors attributed to vehicle drivers. For example, vehicles/driversengaging in positive driving behaviors indicative of safe driving mayreceive higher driver scores, while vehicles/drivers engaging innegative driving behaviors indicative of high-risk driving may receivelower driver scores. According to additional aspects of the disclosure,driver scores also may be calculated or adjusted based on other driverscores received or calculated for nearby vehicles.

Other features and advantages of the disclosure will be apparent fromthe additional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

FIG. 2 is a diagram illustrating various components and devices of adriving analysis system, according to one or more aspects of thedisclosure.

FIG. 3 is a flow diagram illustrating an example method of analyzingvehicle driving data, determining driving behaviors, and calculatingdriver scores using vehicle-to-vehicle communications, according to oneor more aspects of the disclosure.

FIG. 4 is a flow diagram illustrating an example method of calculatingdriver scores based on driver scores of other nearby vehicles, accordingto one or more aspects of the disclosure.

FIGS. 5A-5E are diagrams illustrating examples of various drivingbehaviors that may be detected using vehicle-to-vehicle communications,according to one or more aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a computer system, or a computer program product.Accordingly, those aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment combiningsoftware and hardware aspects. Furthermore, such aspects may take theform of a computer program product stored by one or morecomputer-readable storage media having computer-readable program code,or instructions, embodied in or on the storage media. Any suitablecomputer readable storage media may be utilized, including hard disks,CD-ROMs, optical storage devices, magnetic storage devices, and/or anycombination thereof. In addition, various signals representing data orevents as described herein may be transferred between a source and adestination in the form of electromagnetic waves traveling throughsignal-conducting media such as metal wires, optical fibers, and/orwireless transmission media (e.g., air and/or space).

FIG. 1 illustrates a block diagram of a computing device 101 in drivinganalysis communication system 100 that may be used according to one ormore illustrative embodiments of the disclosure. The driving analysisdevice 101 may have a processor 103 for controlling overall operation ofthe device 101 and its associated components, including RAM 105, ROM107, input/output module 109, and memory 115. The computing device 101,along with one or more additional devices (e.g., terminals 141, 151) maycorrespond to any of multiple systems or devices, such as a drivinganalysis computing devices or systems, configured as described hereinfor transmitting and receiving vehicle-to-vehicle (V2V) communications,analyzing vehicle driving data, determining driving behaviors, andcalculating driver scores, based on the V2V communications.

Input/Output (I/O) 109 may include a microphone, keypad, touch screen,and/or stylus through which a user of the computing device 101 mayprovide input, and may also include one or more of a speaker forproviding audio output and a video display device for providing textual,audiovisual and/or graphical output. Software may be stored withinmemory 115 and/or storage to provide instructions to processor 103 forenabling device 101 to perform various functions. For example, memory115 may store software used by the device 101, such as an operatingsystem 117, application programs 119, and an associated internaldatabase 121. Processor 103 and its associated components may allow thedriving analysis system 101 to execute a series of computer-readableinstructions to transmit or receive vehicle driving data, analyzedriving data and identify driving behaviors, and calculate driverscores.

The driving analysis computing device 101 may operate in a networkedenvironment 100 supporting connections to one or more remote computers,such as terminals/devices 141 and 151. Driving analysis computing device101, and related terminals/devices 141 and 151, may include devicesinstalled in vehicles, mobile devices that may travel within vehicles,or devices outside of vehicles that are configured to receive andprocess vehicle and driving data. Thus, the driving analysis computingdevice 101 and terminals/devices 141 and 151 may each include personalcomputers (e.g., laptop, desktop, or tablet computers), servers (e.g.,web servers, database servers), vehicle-based devices (e.g., on-boardvehicle computers, short-range vehicle communication systems, telematicsdevices), or mobile communication devices (e.g., mobile phones, portablecomputing devices, and the like), and may include some or all of theelements described above with respect to the driving analysis computingdevice 101. The network connections depicted in FIG. 1 include a localarea network (LAN) 125 and a wide area network (WAN) 129, and a wirelesstelecommunications network 133, but may also include other networks.When used in a LAN networking environment, the driving analysiscomputing device 101 may be connected to the LAN 125 through a networkinterface or adapter 123. When used in a WAN networking environment, thedevice 101 may include a modem 127 or other means for establishingcommunications over the WAN 129, such as network 131 (e.g., theInternet). When used in a wireless telecommunications network 133, thedevice 101 may include one or more transceivers, digital signalprocessors, and additional circuitry and software for communicating withwireless computing devices 141 (e.g., mobile phones, short-range vehiclecommunication systems, vehicle telematics devices) via one or morenetwork devices 135 (e.g., base transceiver stations) in the wirelessnetwork 133.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computing devices and drivinganalysis system components described herein may be configured tocommunicate using any of these network protocols or technologies.

Additionally, one or more application programs 119 used by the drivinganalysis computing device 101 may include computer executableinstructions (e.g., driving behavior analysis programs and driver scorealgorithms) for transmitting and receiving vehicle driving data,determining driving behaviors, calculating driver scores for one or morevehicles or drivers, and performing other related functions as describedherein.

As used herein, a driver score (or driving score) may refer to ameasurement of driving abilities, safe driving habits, and other driverinformation. A driver score may be a rating generated by an insurancecompany, financial instruction, or other organization, based on thedriver's age, vision, medical history, driving record, and/or otheraccount data relating to the driver. For example, an insurance companyserver may periodically calculate (i.e., adjust) driver scores for oneor more of the insurance company's customers, and may use the driverscores to perform insurance analyses and determinations (e.g., determinecoverage, calculate premiums and deductibles, award safe driverdiscounts, etc.). As discussed below, a driver score may be calculatedbased on driving data collected by a vehicle sensors and telematicsdevice, and/or additional data received from other nearby vehicles usingvehicle-to-vehicle (V2V) communications. For example, if a driverconsistently drives at a safe following distance, yields appropriatelyto approaching cars, and practices defensive avoidance while driving intraffic, then the driver may be given a high or positive driver score.Alternatively, if a driver regularly tailgates, cuts-off, or races withother cars in traffic, then the driver may be given a low or negativedriver score.

It should be understood that a driver score, as used herein, may beassociated with an individual, group of individuals, or a vehicle. Forinstance, a family, group of friends or co-workers, or other group thatshares one or more vehicles may have a single driver score that isshared by the group. Additionally, a vehicle may have an associateddriver score that is based on one or more primary drivers of the vehicleand can be affected by the driving behavior of any the vehicle'sdrivers. In other examples, a vehicle may be configured to identifydifferent drivers, and each driver of the vehicle may have a separatedriver score.

FIG. 2 is a diagram of an illustrative driving analysis system 200including two vehicles 210 and 220, a driving analysis server 250, andadditional related components. Each component shown in FIG. 2 may beimplemented in hardware, software, or a combination of the two.Additionally, each component of the driving analysis system 200 mayinclude a computing device (or system) having some or all of thestructural components described above for computing device 101.

Vehicles 210 and 220 in the driving analysis system 200 may be, forexample, automobiles, motorcycles, scooters, buses, recreationalvehicles, boats, or other vehicles for which a vehicle driving data maybe analyzed and for which driver scores may be calculated. The vehicles210 and 220 each include vehicle operation sensors 211 and 221 capableof detecting and recording various conditions at the vehicle andoperational parameters of the vehicle. For example, sensors 211 and 221may detect and store data corresponding to the vehicle's location (e.g.,GPS coordinates), speed and direction, rates of acceleration or braking,and specific instances of sudden acceleration, braking, and swerving.Sensors 211 and 221 also may detect and store data received from thevehicle's 210 internal systems, such as impact to the body of thevehicle, air bag deployment, headlights usage, brake light operation,door opening and closing, door locking and unlocking, cruise controlusage, hazard lights usage, windshield wiper usage, horn usage, turnsignal usage, seat belt usage, phone and radio usage within the vehicle,maintenance performed on the vehicle, and other data collected by thevehicle's computer systems.

Additional sensors 211 and 221 may detect and store the external drivingconditions, for example, external temperature, rain, snow, light levels,and sun position for driver visibility. For example, external camerasand proximity sensors 211 and 221 may detect other nearby vehicles,traffic levels, road conditions, traffic obstructions, animals,cyclists, pedestrians, and other conditions that may factor into adriving event data analysis. Sensors 211 and 221 also may detect andstore data relating to moving violations and the observance of trafficsignals and signs by the vehicles 210 and 220. Additional sensors 211and 221 may detect and store data relating to the maintenance of thevehicles 210 and 220, such as the engine status, oil level, enginecoolant temperature, odometer reading, the level of fuel in the fueltank, engine revolutions per minute (RPMs), and/or tire pressure.

Vehicles sensors 211 and 221 also may include cameras and/or proximitysensors capable of recording additional conditions inside or outside ofthe vehicles 210 and 220. For example, internal cameras may detectconditions such as the number of the passengers and the types ofpassengers (e.g. adults, children, teenagers, pets, etc.) in thevehicles, and potential sources of driver distraction within the vehicle(e.g., pets, phone usage, unsecured objects in the vehicle). Sensors 211and 221 also may be configured to collect data a driver's movements orthe condition of a driver. For example, vehicles 210 and 220 may includesensors that monitor a driver's movements, such as the driver's eyeposition and/or head position, etc. Additional sensors 211 and 221 maycollect data regarding the physical or mental state of the driver, suchas fatigue or intoxication. The condition of the driver may bedetermined through the movements of the driver or through other sensors,for example, sensors that detect the content of alcohol in the air orblood alcohol content of the driver, such as a breathalyzer.

Certain vehicle sensors 211 and 221 also may collect informationregarding the driver's route choice, whether the driver follows a givenroute, and to classify the type of trip (e.g. commute, errand, newroute, etc.). In certain embodiments, sensors and/or cameras 211 and 221may determine when and how often the vehicles 210 and 220 stay in asingle lane or stray into other lanes. A Global Positioning System(GPS), locational sensors positioned inside the vehicles 210 and 220,and/or locational sensors or devices external to the vehicles 210 and220 may be used determine the route, lane position, and other vehicleposition/location data.

The data collected by vehicle sensors 211 and 221 may be stored and/oranalyzed within the respective vehicles 210 and 220, and/or may betransmitted to one or more external devices. For example, as shown inFIG. 2, sensor data may be transmitted via short-range communicationsystems 212 and 222 to other nearby vehicles. Additionally, the sensordata may be transmitted via telematics devices 213 and 223 to one ormore remote computing devices, such as driving analysis server 250.

Short-range communication systems 212 and 222 are vehicle-based datatransmission systems configured to transmit vehicle operational data toother nearby vehicles, and to receive vehicle operational data fromother nearby vehicles. In some examples, communication systems 212 and222 may use the dedicated short-range communications (DSRC) protocolsand standards to perform wireless communications between vehicles. Inthe United States, 75 MHz in the 5.850-5.925 GHz band have beenallocated for DSRC systems and applications, and various other DSRCallocations have been defined in other countries and jurisdictions.However, short-range communication systems 212 and 222 need not useDSRC, and may be implemented using other short-range wireless protocolsin other examples, such as WLAN communication protocols (e.g., IEEE802.11), Bluetooth (e.g., IEEE 802.15.1), or one or more of theCommunication Access for Land Mobiles (CALM) wireless communicationprotocols and air interfaces. The vehicle-to-vehicle (V2V) transmissionsbetween the short-range communication systems 212 and 222 may be sentvia DSRC, Bluetooth, satellite, GSM infrared, IEEE 802.11, WiMAX, RFID,and/or any suitable wireless communication media, standards, andprotocols. In certain systems, short-range communication systems 212 and222 may include specialized hardware installed in vehicles 210 and 220(e.g., transceivers, antennas, etc.), while in other examples thecommunication systems 212 and 222 may be implemented using existingvehicle hardware components (e.g., radio and satellite equipment,navigation computers) or may be implemented by software running on themobile devices 215 and 225 of drivers and passengers within the vehicles210 and 220.

The range of V2V communications between vehicle communication systems212 and 222 may depend on the wireless communication standards andprotocols used, the transmission/reception hardware (e.g., transceivers,power sources, antennas), and other factors. Short-range V2Vcommunications may range from just a few feet to many miles, anddifferent types of driving behaviors may be determined depending on therange of the V2V communications. For example, V2V communications rangingonly a few feet may be sufficient for a driving analysis computingdevice 101 in one vehicle to determine that another vehicle istailgating or cut-off the vehicle, whereas longer communications mayallow the device 101 to determine additional types of driving behaviors(e.g., yielding, defensive avoidance, proper response to a safetyhazard, etc.).

V2V communications also may include vehicle-to-infrastructure (V2I)communications, such as transmissions from vehicles to non-vehiclereceiving devices, for example, toll booths, rail road crossings, androad-side traffic monitoring devices. Certain V2V communication systemsmay periodically broadcast data from a vehicle 210 to any other vehicle,or other infrastructure device capable of receiving the communication,within the range of the vehicle's transmission capabilities. Forexample, a vehicle 210 may periodically broadcast (e.g., every 0.1second, every 0.5 seconds, every second, every 5 seconds, etc.) certainvehicle operation data via its short-range communication system 212,regardless of whether or not any other vehicles or reception devices arein range. In other examples, a vehicle communication system 212 mayfirst detect nearby vehicles and receiving devices, and may initializecommunication with each by performing a handshaking transaction beforebeginning to transmit its vehicle operation data to the other vehiclesand/or devices.

The types of vehicle operational data, or vehicle driving data,transmitted by vehicles 210 and 220 may depend on the protocols andstandards used for the V2V communication, the range of communications,and other factors. In certain examples, vehicles 210 and 220 mayperiodically broadcast corresponding sets of similar vehicle drivingdata, such as the location (which may include an absolute location inGPS coordinates or other coordinate systems, and/or a relative locationwith respect to another vehicle or a fixed point), speed, and directionof travel. In certain examples, the nodes in a V2V communication system(e.g., vehicles and other reception devices) may use internal clockswith synchronized time signals, and may send transmission times withinV2V communications, so that the receiver may calculate its distance fromthe transmitting node based on the difference between the transmissiontime and the reception time. The state or usage of the vehicle's 210controls and instruments may also be transmitted, for example, whetherthe vehicle is accelerating, braking, turning, and by how much, and/orwhich of the vehicle's instruments are currently activated by the driver(e.g., head lights, turn signals, hazard lights, cruise control, 4-wheeldrive, traction control, etc.). Vehicle warnings such as a detection bythe vehicle's 210 internal systems that the vehicle is skidding, that animpact has occurred, or that the vehicle's airbags have been deployed,also may be transmitted in V2V communications. In various otherexamples, any data collected by any vehicle sensors 211 and 221potentially may be transmitted via V2V communication to other nearbyvehicles or infrastructure devices receiving V2V communications fromcommunication systems 212 and 222. Further, additional vehicle drivingdata not from the vehicle's sensors (e.g., vehicle make/model/yearinformation, driver insurance information, driving route information,vehicle maintenance information, driver scores, etc.) may be collectedfrom other data sources, such as a driver's or passenger's mobile device215 or 225, driving analysis server 250, and/or another externalcomputer system 230, and transmitted using V2V communications to nearbyvehicles and other receiving devices using communication systems 212 and222.

As shown in FIG. 2, the data collected by vehicle sensors 211 and 221also may be transmitted to a driving analysis server 250, and one ormore additional external servers and devices via telematics devices 213and 223. Telematics devices 213 and 223 may be computing devicescontaining many or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1. As discussed above, thetelematics devices 213 and 223 may receive vehicle operation data anddriving data from vehicle sensors 211 and 221, and may transmit the datato one or more external computer systems (e.g., driving analysis server250 of an insurance company, financial institution, or other entity)over a wireless transmission network. Telematics devices 213 and 223also may be configured to detect or determine additional types of datarelating to real-time driving and the condition of the vehicles 210 and220. In certain embodiments, the telematics devices 213 and 223 maycontain or may be integral with one or more of the vehicle sensors 211and 221. The telematics devices 213 and 223 also may store the type oftheir respective vehicles 210 and 220, for example, the make, model,trim (or sub-model), year, and/or engine specifications, as well asother information such as vehicle owner or driver information, insuranceinformation, and financing information for the vehicles 210 and 220.

In the example shown in FIG. 2, telematics devices 213 and 223 mayreceive vehicle driving data from vehicle sensors 211 and 221, and maytransmit the data to a driving analysis server 250. However, in otherexamples, one or more of the vehicle sensors 211 and 221 may beconfigured to transmit data directly to a driving analysis server 250without using a telematics device. For instance, telematics devices 213and 223 may be configured to receive and transmit data from certainvehicle sensors 211 and 221, while other sensors may be configured todirectly transmit data to a driving analysis server 250 without usingthe telematics device 216. Thus, telematics devices 213 and 223 may beoptional in certain embodiments.

In certain embodiments, mobile computing devices 215 and 225 within thevehicles 210 and 220 may be used to collect vehicle driving data and/orto receive vehicle driving data from sensors 211 and 221, and then totransmit the vehicle driving data to the driving analysis server 250 andother external computing devices. Mobile computing devices 215 and 225may be, for example, mobile phones, personal digital assistants (PDAs),or tablet computers of the drivers or passengers of vehicles 210 and220. Software applications executing on mobile devices 215 and 225 maybe configured to detect certain driving data independently and/or maycommunicate with vehicle sensors 211 and 221 to receive additionaldriving data. For example, mobile devices 215 and 225 equipped with GPSfunctionality may determine vehicle location, speed, direction and otherbasic driving data without needing to communicate with the vehiclesensors 211 or 221, or any vehicle system. In other examples, softwareon the mobile devices 215 and 225 may be configured to receive some orall of the driving data collected by vehicle sensors 211 and 221.

When mobile computing devices 215 and 225 within the vehicles 210 and220 are used to detect vehicle driving data and/or to receive vehicledriving data from vehicles 211 and 221, the mobile computing devices 215and 225 may store, analyze, and/or transmit the vehicle driving data toone or more other devices. For example, mobile computing devices 215 and225 may transmit vehicle driving data directly to one or more drivinganalysis servers 250, and thus may be used in conjunction with orinstead of telematics devices 213 and 223. Additionally, mobilecomputing devices 215 and 225 may be configured to perform the V2Vcommunications described above, by establishing connections andtransmitting/receiving vehicle driving data to and from other nearbyvehicles. Thus, mobile computing devices 215 and 225 may be used inconjunction with or instead of short-range communication systems 212 and222 in some examples. Moreover, the processing components of the mobilecomputing devices 215 and 225 may be used to analyze vehicle drivingdata, determine driving behaviors, calculate driver scores, and performother related functions. Therefore, in certain embodiments, mobilecomputing devices 215 and 225 may be used in conjunction with, or inplace of, the driving analysis modules 214 and 224.

Vehicles 210 and 220 may include driving analysis modules 214 and 224,which may be separate computing devices or may be integrated into one ormore other components within the vehicles 210 and 220, such as theshort-range communication systems 212 and 222, telematics devices 213and 223, or the internal computing systems of vehicles 210 and 220. Asdiscussed above, driving analysis modules 214 and 224 also may beimplemented by computing devices independent from the vehicles 210 and220, such as mobile computing devices 215 and 225 of the drivers orpassengers, or one or more separate computer systems 230 (e.g., a user'shome or office computer). In any of these examples, the driving analysismodules 214 and 224 may contain some or all of the hardware/softwarecomponents as the computing device 101 depicted in FIG. 1. Further, incertain implementations, the functionality of the driving analysismodules, such as storing and analyzing vehicle driving data, determiningdriving behaviors, and calculating driving scores, may be performed in acentral driving analysis server 250 rather than by individual vehicles210 and 220. In such implementations, the vehicles 210 and 220 mightonly collect and transmit vehicle driving data to a driving analysisserver 250, and thus the vehicle-based driving analysis modules 214 and224 may be optional.

Driving analysis modules 214 and 224 may be implemented in hardwareand/or software configured to receive vehicle driving data from vehiclesensors 211 and 221, short-range communication systems 212 and 222,telematics devices 213 and 223, and/or other driving data sources. Afterreceiving the vehicle driving data, driving analysis modules 214 and 224may perform a set of functions to analyze the driving data, determinedriving behaviors, and calculate driver scores. For example, the drivinganalysis modules 214 and 224 may include one or more driving behavioranalysis/driver score calculation algorithms, which may be executed bysoftware running on generic or specialized hardware within the drivinganalysis modules. The driving analysis module 214 in a first vehicle 210may use the vehicle driving data received from that vehicle's sensors211, along with vehicle driving data for other nearby vehicles receivedvia the short-range communication system 212, to determine drivingbehaviors and calculate driver scores applicable to the first vehicle210 and the other nearby vehicles. Within the driving analysis module214, a driver score calculation function may use the results of thedriving analysis performed by the module 214 to calculate/adjust driverscores for a driver of a vehicle 210 or other vehicles based ondetermined driving behaviors. Further descriptions and examples of thealgorithms, functions, and analyses that may be executed by the drivinganalysis modules 214 and 224 are described below in reference to FIGS. 3and 4.

The system 200 also may include a driving analysis server 250,containing some or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1. The driving analysis server 250may include hardware, software, and network components to receivevehicle driving data from one or more vehicles 210 and 220, and otherdata sources. The driving analysis server 250 may include a driving dataand driver score database 252 and driving analysis module 251 torespectively store and analyze driving data received from vehicles andother data sources. The driving analysis server 250 may initiatecommunication with and/or retrieve driving data from vehicles 210 and220 wirelessly via telematics devices 213 and 223, mobile devices 215and 225, or by way of separate computing systems (e.g., computer 230)over one or more computer networks (e.g., the Internet). Additionally,the driving analysis server 250 may receive additional data relevant todriving behavior determinations and driver score calculations from othernon-vehicle data sources, such as external traffic databases containingtraffic data (e.g., amounts of traffic, average driving speed, trafficspeed distribution, and numbers and types of accidents, etc.) at varioustimes and locations, external weather databases containing weather data(e.g., rain, snow, sleet, and hail amounts, temperatures, wind, roadconditions, visibility, etc.) at various times and locations, and otherexternal data sources containing driving hazard data (e.g., roadhazards, traffic accidents, downed trees, power outages, roadconstruction zones, school zones, and natural disasters, etc.)

Data stored in the driving data and driver score database 252 may beorganized in any of several different manners. For example, a table indatabase 252 may contain all of the vehicle operation data for aspecific vehicle 210, similar to a vehicle event log. Other tables inthe database 252 may store certain types of data for multiple vehicles.For instance, tables may store specific driving behaviors andinteractions (e.g., accidents, tailgating, cutting-off, yielding,racing, defensive avoidances, etc.) for multiples vehicles. Vehicledriving data may also be organized by time and/or place, so that thedriving behaviors or interactions between multiples vehicles 210 and 220may be stored or grouped by time and location.

The driving analysis module 251 within the driving analysis server 250may be configured to retrieve data from the driving data and driverscore database 252, or may receive driving data directly from vehicles210 and 220 or other data sources, and may perform driving dataanalyses, driving behavior determinations, driver score calculations,and other related functions. The functions performed by the drivinganalysis module 251 may be similar to those of driving analysis modules214 and 224, and further descriptions and examples of the algorithms,functions, and analyses that may be executed by the driving analysismodule 251 are described below in reference to FIGS. 3 and 4.

In various examples, the driving data analyses, driving behaviordeterminations, and driving score calculations may be performed entirelyin the driving analysis module 251 of the driving analysis server 250(in which case driving analysis modules 214 and 224 need not beimplemented in vehicles 210 and 220), or may be performed entirely inthe vehicle-based driving analysis modules 214 and 224 (in which casethe driving analysis module 251 and/or the driving analysis server 250need not be implemented). In other examples, certain driving dataanalyses may be performed by vehicle-based driving analysis modules 214and 224, while other driving data analyses are performed by the drivinganalysis module 251 at the driving analysis server 250. For example, avehicle-based driving analysis module 214 may continuously receive andanalyze driving data from nearby vehicles to determine certain drivingbehaviors (e.g., tailgating, cutting-off, yielding, etc.) so that largeamounts of driving data need not be transmitted to the driving analysisserver 250. However, after a positive or negative driving behavior isdetermined by the vehicle-based driving analysis module 214, thebehavior may be transmitted to the server 250, and the driving analysismodule 251 may determine if a driver score should be updated based onthe determined driving behavior.

FIG. 3 is a flow diagram illustrating an example method of performingdriving behavior determinations and driver scores calculations based onanalyses of vehicle driving data from vehicle-to-vehicle communications.This example method may be performed by one or more computing devices ina driving analysis system, such as vehicle-based driving analysismodules 214 and 224, a driving analysis module 251 of a driving analysisserver 250, user mobile computing devices 215 and 225, and/or othercomputer systems.

The steps shown in FIG. 3 describe performing an analysis to determinedriving behaviors between vehicles using V2V communications, and thencalculating or adjusting driver scores based on the determined drivingbehaviors. Driving behaviors may include any number of identifiable“social interactions” between two or more vehicles on the road,including negative behaviors such as tailgating, cutting-off,brake-checking, preventing another vehicle from merging, and racing, orpositive behaviors such as yielding, defensive avoidance, or allowinganother vehicle to merge.

Occurrences of negative driving behaviors may indicate a high-risk orunsafe driver, while occurrences of positive driving behaviors mayindicate a low-risk or safe driver. In some cases, a first vehicle 210might not be actively involved in a driving behavior, but may beinvolved only as an object of another vehicle's 220 driving behavior(e.g., a vehicle 210 being tailgated by another vehicle 220, or avehicle 210 allowed to merge by another vehicle 220), in which case thedetermination of the driving behavior may be neither positive nornegative with respect to vehicle 210.

In step 301, vehicle driving data may be received for a first vehicle210, corresponding to data from the vehicle's sensors 211. As describedabove in reference to FIG. 2, a driving analysis module 214 withinvehicle 210 may receive and store vehicle driving data from thevehicle's internal computer systems and any combination of the vehicle'ssensors 211. The data received in step 301 may include, for example, thelocation, speed, and direction of the vehicle 210, object proximity datafrom the vehicle's external cameras and proximity sensors, and data fromthe vehicle's various systems used to determine if the vehicle 210 isbraking, accelerated, or turning, etc., and to determine the status ofthe vehicle's user-operated controls (e.g., head lights, turn signals,hazard lights, radio, phone, etc.), along with any other data collectedby vehicle sensors 211.

In step 302, vehicle driving data may be received for a second vehicle220, corresponding to data transmitted via V2V communications. Asdescribed above, vehicle driving data may be transmitted from the secondvehicle 220 to the first vehicle 210 using short-range communicationssystems 212 and 222, user mobile devices 215 and 225, or other wirelesstransmission techniques. In certain examples, DSRC protocols andstandards may be used, in which vehicle 220 may periodically broadcast aset of vehicle driving data to any vehicles or other receiving deviceswithin its broadcast range. In some examples, driving data transmittedby vehicle 220 using V2V communication may include basic vehiclelocation, speed, and trajectory data, such as the GPS coordinates, speedand direction of travel, rate of acceleration or deceleration, and ratesof turning data of the vehicle 210. However, the V2V communications mayinclude additional data in various other examples, and may potentiallyinclude any or all of the data collected from the vehicle's sensors 221.Additionally, after two vehicles 210 and 220 have established acommunication link via short-range communication systems 212 and 222,the vehicles may transmit their bearings (or relative direction)vis-à-vis the other vehicle in V2V communications. In other examples,the first vehicle 210 may determine the bearing of a second nearbyvehicle 220 by storing and analyzing multiple V2V transmissions from thevehicle 220 over a period of time.

In step 303, the vehicle driving data received in steps 301 and 302 maybe analyzed, and driving behaviors may be determined for the vehicles210 and 220 based on the driving data. For example, a driving analysismodule 214 in a first vehicle 210 may compare the driving data (e.g.,location, speed, direction) from its own vehicle sensors 211 (receivedin step 301) with the corresponding driving data (e.g., location, speed,direction) from a nearby vehicle 220 (received in step 302). Based onthe relative locations, speeds, and directions of travel of vehicles 210and 220, the driving analysis module 214 may determine a drivingbehavior involving the two vehicles.

FIGS. 5A-5E illustrate examples of different “social interactions”between two vehicles that may characterize different driving behaviorsin step 303. In FIG. 5A, an example of tailgating is shown in whichvehicle 510 is tailgating vehicle 520. A driving analysis module 214 ineither vehicle 510 or 520 may detect tailgating in step 303 by comparingthe relative positions, speeds, and distances between the two vehiclesover a period of time. One or more driving behavior algorithms executedby a driving analysis module 214 may define tailgating in terms ofvehicle speed and following distance. For example, a tailgatingalgorithm may determine that a vehicle is tailgating (T) if itsfollowing distance in feet (D), is less than its velocity inmiles-per-hour (V) times a tailgating factor, such as: [If D<V, then T],[If D>V*1.1, then T], [If D<V*1.5, then T], or [If D <V*2, then T], etc.The amount of time that a vehicle is tailgating may also factor into adetermination of a tailgating behavior. For example, if the drivinganalysis module 214 determines that a vehicle's tailgating exceeds atime threshold, which may be consecutive number of seconds tailgating(e.g., 5 seconds, 10 seconds, 30 seconds, etc.), a percentage of drivingtime tailgating (e.g., 10%, 20%, 50%, etc.), or a total amount oftailgating time in an hour, day, or driving trip (e.g., 1 minute, 5minutes, 10 minutes, etc.), then the driving analysis module 214 maydetermine that the vehicle has engaged in a tailgating driving behavior.

In FIG. 5B, an example of defensive avoidance is shown, in which vehicle520 changes lanes to avoid being tailgated by vehicle 510. A drivinganalysis module 214 in either vehicle 510 or 520 may detect defensiveavoidance by vehicle 520 in step 303, by executing one or morealgorithms that define a defensive avoidance driving behavior. Forexample, if a vehicle is being tailgated (as defined by one or moretailgating algorithms) for at least a minimum time threshold (e.g., 1second, 5 seconds, 10 seconds, etc.), and then the vehicle beingtailgated safely changes lanes to eliminate the tailgating situation,then the driving analysis module 214 may determine that the vehicle hasengaged in a defensive avoidance driving behavior. Determinations ofdefensive avoidance by driving analysis modules 214 also may take intoaccount traffic density. For example, when a current traffic density isgreater than a predetermined density threshold, the amount of time thatvehicle 520 is given to change lanes in order to count as a defensiveavoidance driving behavior may be increased.

In FIG. 5C, an example is shown in which vehicle 520 has cut-off vehicle510, by changing lanes closely in front of vehicle 510. A drivinganalysis module 214 may detect cutting-off in step 303 by comparing therelative positions and distances between the two vehicles over a periodof time. For example, one or more driving behavior algorithms may definecutting-off as occurrence of a lane change immediately after which thefollowing vehicle is in a tailgating position (as defined by one or moretailgating algorithms). For instance, under the tailgating algorithm [IfD>V, then T], if vehicle 520 changes lanes in front of vehicle 510 whenboth cars are traveling 60 MPH, and the distance between the twovehicles immediately after the lane change is less than 60 feet, thenthe driving analysis module 214 may determine that vehicle 520 hascut-off vehicle 510. In certain implementations, the following vehicle510 may be provided a tailgating grace period (e.g., 5 seconds, 10seconds, etc.) after being cut-off, to allow it increase its followingdistance, before it can be assessed (or begin to be assessed) with atailgating driving behavior.

In FIG. 5D, an example of yielding is shown in which vehicle 510 yieldsto vehicle 520, allowing vehicle 520 to merge into the lane of vehicle510. As with tailgating and cutting-off, a driving analysis module 214may determine yielding in step 303 by comparing the relative positions,speeds, and distances between the two vehicles over a period of time.For example, if vehicle 520 expresses an intention to change into thesame lane as vehicle 510, and vehicle 510 maintains or reduces speed tosafely allow the lane change, then driving analysis module 214 maydetermine that vehicle 510 has performed a yielding driving behavior.Expressions of intention to change lanes may be determined by, forexample, based on speed matching by a vehicle 520 in an adjacent lane,turn signal usage of a vehicle 520 in an adjacent lane (using turnsignal data and other vehicle control data transmitted in V2Vcommunications), the ending of an upcoming lane in traffic (using laneending determinations by vehicle sensors, GPS and navigation data, orother techniques). After a driving analysis module 214 identifies anintention of a nearby vehicle to change lanes, if the vehicle 510 slowsdown or maintains its speed, so that its following distance is increasedto exceed a yielding distance threshold (e.g., V*1.5, V*2, etc.), or sothat after the lane change is completed then vehicle 520 will not be ina tailgating position, then the vehicle 510 may be attributed with apositive yielding driving behavior. To the contrary, if vehicle 510speeds up or decreases its current following distance to prevent thelane change, then vehicle 510 may be attributed with a negative drivingbehavior for preventing the merging of vehicle 520.

In FIG. 5E, an example is shown of racing by vehicles 510 and 520. As inthe examples above, a driving analysis module 214 may detect racing instep 303 by comparing the relative positions, speeds, and distancesbetween the two vehicles 510 and 520 over a period of time, as well asdata from other vehicles 530 and 540, and other data sources. Forexample, one or more driving behavior algorithms may define racing as anoccurrence of two or more vehicles 510 and 520 in close proximity to oneanother (for example, using a proximity threshold, e.g., 100 feet, 0.25miles, 0.5 miles, 1 mile, etc.), over a period of time (e.g., 30seconds, 1 minute, etc.), and when the vehicles 510 and 520 are movingfaster than the other traffic on the same road by more than a racingspeed threshold (e.g., 25% faster, 50% faster, etc.).

In addition to the driving behaviors described above, and the variousexamples of algorithms and thresholds used to determine occurrences ofthese driving behaviors, it should be understood that other types ofdriving behaviors may be detected using V2V communications, and thatvarious other driving behavior determination algorithms and differentthreshold values may be used as well. Additionally, the drivingbehaviors described above, or other driving behaviors determined in step303 may use multiple algorithms and/or thresholds to determine degreesof magnitude for a driving behavior. For example, when determiningnegative driving behaviors such as tailgating, cutting-off, and racing,a driving analysis module 214 may use different thresholds to determinedifferent levels of severity of the negative behavior. For instance,tailgating under the definition of [If D<V*1.5, then T] for between 5-10seconds may be considered a minor tailgating behavior, whereastailgating under the definition of [If D<V*0.7, then T] for more than aminute consecutively may be considered a severe tailgating behavior, andso on.

In step 304, one or more driver scores may be calculated based on thedriving behaviors determined in step 303. As discussed above, driverscores may correspond to ratings by insurance companies, financialinstitutions, or other organizations of the driving abilities, safedriving habits, and other information for a driver or a related group ofdrivers (e.g., family, roommates, co-workers, or other group of driversassociated with the same vehicle or vehicles). Driver scores may be usedto help obtain vehicle financing and determine insurance, rates,coverage, and discounts. If a driving analysis module 214 determines a“negative” (i.e., unsafe or risky) driving behavior for a driver ofvehicle 210 in step 303, then the driving analysis module 214 maynegatively adjust the driver's driver score in step 304. Similarly, ifthe driving analysis module 214 determines a “positive” or safe drivingbehavior in step 303, then the driving analysis module 214 maypositively adjust the driver score in step 304. When calculating oradjusting a driver score based on determined driving behaviors,behaviors of greater magnitude (e.g., severe tailgating or racing) maybe weighed more heavily than less severe behaviors (e.g., minortailgating or failure to yield to allow a lane change in traffic).Additionally, minor driving behaviors might not cause any adjustments indriver scores, and some positive and negative behaviors may cancel outso that the driver score might not be adjusted. In some cases, alloccurrences of all determined positive and negative driving behaviorsmay be accumulated and stored over a period of time, such a week, month,year, or for an insurance term, and the accumulated set of drivingbehaviors may be used to calculate insurance rate adjustments ordiscounts, along with other factors such as accidents, vehiclemaintenance, and driving record. When a specific driver of a vehicle 210is known, the driving analysis module 214 may calculate or update adriver score for that specific driver. Otherwise, the driving analysismodule 214 may calculate or update a driver score corresponding to thevehicle 210 and/or multiple driver scores for different drivers of thevehicle.

As shown in FIG. 3, a single vehicle-based driving analysis module 214may receive driving data for a first vehicle 210 (step 301), may receiveV2V communications including driving data for one or more other vehicles(step 302), may determine driving behaviors (step 303), and maycalculate or update driver scores (step 304) for the first vehicle 210.However, other driving analysis modules and/or other computing devicesmay be used to some or all of the steps and functionality describedabove in reference to FIG. 3. For example, any of steps 301-304 may beperformed by a user's mobile device 215 or 225 within the vehicles 210or 220. These mobile devices 215 or 225, or another computing device230, may execute software configured to perform similar functionality inplace of the driving analysis modules 214 and 224. Additionally, some orall of the driving analysis functionality described in reference to FIG.3 may be performed by a driving analysis module 251 at a non-vehiclebased driving analysis server 250. For example, vehicles 210 and 220 maybe configured to transmit their own vehicle sensor data, and/or the V2Vcommunications data received from other nearby vehicles, to a centraldriving analysis server 250 via telematics devices 213 and 223. In thisexample, the driving analysis module 251 of the server 250 may performthe data analysis, determinations of driving behaviors, and driver scorecalculations for any vehicles 210 and 220 for which it receives drivingdata.

In some examples, certain functionality may be performed invehicle-based driving analysis modules 214 and 224, while otherfunctionality may be performed by the driving analysis module 251 at thedriving analysis server 250. For instance, vehicle-based drivinganalysis modules 214 and 224 may continuously receive and analyzedriving data for their own vehicles 210 and 220 and nearby vehicles (viaV2V communications), and may determine driving behaviors (e.g.,tailgating, cutting-off, yielding, racing, etc.) for their own vehicles210 and 220 and/or the other nearby vehicles. After the vehicle-baseddriving analysis modules 214 and 224 have determined the drivingbehaviors, indications of these behaviors may be transmitted to theserver 250 so that the driving analysis module 251 can perform thedriver score calculations and updates based on the driving behaviors.For instance, vehicles 210 and 220 both may detect a negative drivingbehavior for a third vehicle, and may report the negative behavior forthe third vehicle to the driving analysis server 250, which may accessother vehicle and driver information for the third vehicle and maypotentially adjust a driver score for the third vehicle based on thedriving behaviors reported by vehicles 210 and 220. Additionally, insome examples, a first vehicle 210 (or other V2V receivinginfrastructure device, such a roadside receiver at a tollbooth ortraffic monitor) may receive V2V communications from multiple othervehicles and determine driving behaviors for those other vehicles, evenwhen the first vehicle 210 (or other receiving device) is not directlyinvolved in the driving behavior. In such cases, indications of thedetermined driving behaviors may be transmitted to the vehicles involvedand/or to an external system (e.g., driving analysis server 250) for thecalculation and implementation of driver scores for the vehiclesinvolved. Additionally, in some embodiments, any analysis that might beperformed at the driving analysis server 250 may be performed insteadwithin the vehicles, for example, in driving analysis modules 214 and/or224. For instance, a first vehicle may analyze the driving behaviors ofa second vehicle and transmit the determined driving behavior data tothe second vehicle and/or additional vehicles. Thus, the drivinganalysis server 250 may be optional in certain embodiments, and some orall of the driving analyses may be performed within the vehiclesthemselves.

V2V communication may be used to analyze driving interactions anddriving behaviors between two vehicles (e.g., vehicles 210 and 220), asdiscussed above. In other examples, similar techniques may be used toanalyze driving interactions and driving behaviors between three or morevehicles. For instance, racing between three or more vehicles may bedetected using similar techniques of V2V communication between each ofthe vehicles. Additional complex driving interactions may be detectedusing V2V communications between three or more vehicles. For example, avehicle in a one lane may drift or change into a second lane, which maycause a vehicle in the second lane to swerve or change lanes into athird lane, etc. These behaviors may be detected and used to identifywhen certain behaviors (e.g., cutting off, swerving, tailgating) are andare not occurring. Other complex traffic interactions also may bedetected and analyzed using V2V communications among groups of vehicle,for example, analyzing the driving behaviors of many drivers at a busyintersection, or in response to a lane starting or ending on a busystreet or highway, or other complex traffic interactions.

In some examples, driving interactions may be detected, and drivingbehaviors may be determined, regarding how drivers respond to nearbyaggressive drivers, nearby passive drivers, nearby save drivers, nearbydistracted drivers, and the like. For instance, an aggressive driver ordistracted driver may cause other drivers nearby to act overlyaggressive in response, while a passive and courteous driver may causeother nearby drivers to drive safely in response. Such responses may bedetected using V2V communications, and driving analyses/driver scorecalculations may be based on such responses.

FIG. 4 is a flow diagram illustrating an example method of calculatingdriver scores based on the driver scores of nearby vehicles. In step401, one or more driver scores may be received or calculated forvehicles nearby a first vehicle, using V2V communications. For example,while vehicle 210 is being driven, its short-range communication system212 may be used to initiate communication links and receive vehicledriving data via V2V communications from other vehicles near vehicle210. As discussed above, DSRC protocols and standards may be used forV2V communications in some systems, while other various V2Vcommunication hardware, techniques, and protocols may be used in othersystems.

Using the vehicle driving data received from other vehicles over V2Vcommunications, the vehicle 210 may calculate driver scores for theother vehicles. As described in reference to FIG. 3, a driving analysismodule 214 within the vehicle 210 may calculate or update its own driverscores (e.g., for the driver of the vehicle 210) and/or for the othernearby vehicles by using V2V communications to detect “socialinteractions” between vehicles that may characterize positive ornegative driving behaviors, such as tailgating and cutting-off(negative), or yielding and defensive avoidance (positive). Thus, step401 may correspond to some or all of steps 301-304, and may includereceiving driving data from vehicle sensors (step 301), receiving V2Vcommunications from other nearby vehicles (step 302), determiningdriving behaviors (step 303), and/or calculating driver scores for theother nearby vehicles based on the determined driving behaviors (step304).

When a vehicle-based driving analysis module 214 calculates driverscores for other vehicles, these scores may be less complete and/or lessaccurate than when the driving analysis module 214 calculates driverscores for its own vehicle 210, or when a driving analysis module 251 atan external server 250 calculates driver scores for its associatedvehicles. For instance, a vehicle 210 attempting to calculate a driverscore for another vehicle 220 on the same road may have only a smallamount of data and a few limited interactions on which to base thisdriver score. Therefore, the driver scores calculated for other nearbyvehicles in step 401 may be classified using a relatively simple drivingscale (e.g., binary, numerical, etc.). If a nearby vehicle is speeding,weaving, tailgating, racing, or engaging in other negative drivingbehaviors, then the vehicle may be assigned a low driver score (e.g.,“Bad Driver,” 1 out of 5, etc.). In contrast, if another nearby vehicleis obeying the speed limit and traffic laws, following at a safedistance, yielding, practicing defensive avoidance, and engaging inother positive driving behaviors, then the vehicle may be assigned ahigh driver score (e.g., “Good Driver,” 5 out of 5, etc.).

In some examples, additional information may be used to calculate driverscores for other vehicles in step 401 in order to increase the accuracyof the driver scores. For example, a vehicle 210 may receive additionalvehicle identifying information for other nearby vehicles over the V2Vcommunication link, such as the vehicle's make, model, year, VIN,insurance information, driver information, and/or owner information. Thedata analysis module 214 of the vehicle 210 may use this additionalinformation to perform a more accurate driver score calculation, or maytransmit this information to the driving analysis server 250, which mayperform the driver score calculations for the other vehicles afteraccessing driving records, insurance records, and any other availableinformation regarding the other vehicles or drivers.

In other examples, driver scores may be transmitted from one vehicle toanother using V2V communications. For instance, along with its basicvehicle location and trajectory information, and various other sensorand vehicle control data, vehicles 210 and 220 may be configured tostore and transmit a current driver score associated with the driver orvehicle to nearby vehicles using V2V communications. A vehicle may storeand maintain its current driver score(s) internally, using drivinganalysis module 214 or on mobile device 215, or may periodically receiveupdated driver scores from driving analysis server 250 via telematicsdevice 213. In these cases, the vehicle 210 need not determine drivingbehaviors and calculate driver scores for other nearby vehicles, butrather can just receive and store the driver scores sent by othervehicles in V2V communications.

After determining driver scores for other nearby vehicles in step 401,one or more determinations may be performed in step 402 whether thefirst vehicle 210 typically drives among high-risk or low-risk driversor vehicles in step 402. The determinations of step 402 may be performedby a vehicle-based driving analysis module 214, or a mobile device 215,or by a driving analysis module 251 and an external server 250.

To determine whether or not a vehicle 210 typically drives amonghigh-risk or low-risk drivers, the driver scores determined in step 401may be collected and analyzed over a number of driving trips or a periodof time (e.g., over the previous 10 driving hours, 100 driving hours,etc., or over the previous week, month, or year, etc.). The averagedriver scores and/or other driver score statistics for the vehiclesdriving near the first vehicle 210 may be compared to one or morehigh-risk and low-risk thresholds. For example, if vehicle 210 spendsmore than N hours/week (e.g., 5 hours/week, 10 hours/week, etc.), or N %of its driving time (e.g., 25%, 50%, etc.) driving within range ofvehicles with low driver scores, then the driving analysis module 214may determine that a high-risk driver threshold has been satisfied (402:High-Risk). In contrast, if vehicle 210 spends less than N hours/week(e.g., 2 hours/week, 5 hours/week, etc.), or N % of its driving time(e.g., 5%, 10%, etc.) near vehicles with low driver scores, then thedriving analysis module 214 may determine that a low-risk driverthreshold has been satisfied (402: Low-Risk). Because both a high-riskand a low-risk threshold are used in this example, the driving analysismodule 214 may determine that neither threshold is satisfied and thatthe first vehicle 210 is typically driven among average-risk traffic,without a predominance of high-risk or low-risk driver scores for nearbyvehicles (402: Neither).

In steps 403-405, a driver score for the first vehicle 210 (or thedriver of the first vehicle 210) may be updated based on thedetermination in step 402 of the types of drivers/vehicles among whichthe first vehicle 210 is frequently driven. If it is determined in step402 the first vehicle 210 is frequently driven among high-riskvehicles/drivers (402: High-Risk), then the driver score for the firstvehicle 210 may be decreased in step 403 to reflect the higher risksassociated with frequently driving among high-risk drivers and vehicles.Similarly, if it is determined that the first vehicle 210 is frequentlydriven among low-risk vehicles/drivers, or is not frequently drivenamong high-risk vehicles/drivers (402: Low-Risk), then the driver scorefor the first vehicle 210 may be increased in step 404 to reflect thehigher risks associated with frequently driving among high-risk driversand vehicles. If neither the high-risk nor the low-risk driverthresholds are met (402: Neither), possibly indicating that the vehicle210 is driven an average amount and in average driving conditions, andnot predominately around high-risk or low-risk vehicles/drivers, thenthe driver score for the first vehicle 210 may be maintained at itscurrent level in step 405.

Although the examples above describe setting or changing a driver scorebased on the nearby presence of high-risk or low-risk vehicles (ordrivers), other calculations may use the presence of high-risk orlow-risk vehicles (or drivers) as just one of many factors affectingdriver scores calculations. For example, a driver score may becalculated using various equations or algorithms that take into accountone or more of a personal driving score (e.g., a measurement of thedirect driving behaviors of the driver), a personal exposure value(e.g., a measurement of time, mileage, etc. that the driver is drivingnear bad or high-risk drivers), a bad/high-risk driver reaction value(e.g., a measurement of how the driver reacts to nearby bad or high-riskdrivers), an influence value (e.g., a measurement of how much thedriver's behavior is influenced by the behavior of other drivers),and/or a location diversity value (e.g., a measurement of how often thedriver drives in the same area), and other related factors. Variousdifferent driver score calculations may be implemented using some or allof these values, alone and/or combined in various different algorithms.Additionally, as discussed above, any of the above values ormeasurements may be calculated for drivers, groups of drivers, orvehicles.

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention.

The invention claimed is:
 1. A driving analysis computing devicecomprising: a processor; and a memory storing computer-executableinstructions which, when executed by the processor, cause the drivinganalysis computing device to: determine vehicle driving data of a firstvehicle using a plurality of vehicle sensors; receive vehicle drivingdata of a second vehicle from the second vehicle usingvehicle-to-vehicle communications; determine a vehicle driving behaviorof the first vehicle by performing an analysis of at least a firstportion of the first vehicle driving data; determine a vehicle drivingbehavior of the second vehicle, based on an analysis of at least thesecond vehicle driving data; determine whether the vehicle drivingbehavior of the second vehicle was performed within a threshold amountof time or distance of the vehicle driving behavior of the firstvehicle; and apply either a positive impact or a negative impact on thesecond vehicle or a driver of the second vehicle in response todetermining that the vehicle driving behavior of the second vehicle wasperformed within the threshold amount of time or distance of the vehicledriving behavior of the first vehicle.
 2. The driving analysis computingdevice of claim 1, wherein the instructions, when executed, furthercause the driving analysis computing device to: determine a driver scorefor the first vehicle or the second vehicle, or a driver of the first orsecond vehicle, based on the corresponding determined vehicle drivingbehavior, the driver score being a measurement of driving abilities. 3.The driving analysis computing device of claim 1, wherein the pluralityof vehicle sensors are located in the first vehicle, and wherein thesecond vehicle driving data is transmitted from the second vehicle tothe first vehicle, the second vehicle driving data comprising at leastone of: a speed of the second vehicle; a position of the second vehicle;or a direction of travel of the second vehicle.
 4. The driving analysiscomputing device of claim 1, wherein the second vehicle driving datatransmitted from the second vehicle comprises a driver score associatedwith the second vehicle or the driver of the second vehicle, the driverscore being generated based on a plurality of vehicle behaviors of thesecond vehicle or the driver of the second vehicle.
 5. The drivinganalysis computing device of claim 1, wherein determining the vehicledriving behavior of the first vehicle comprises: determining a firstrelative position of the second vehicle with respect to the firstvehicle at a first time; and determining a second relative position ofthe second vehicle with respect to the first vehicle at a second timeafter the first time.
 6. The driving analysis computing device of claim5, wherein determining the vehicle driving behavior of the first vehiclecomprises: determining that the first vehicle tailgated the secondvehicle based on the first and second relative positions and the firstand second times, wherein determining the vehicle driving behavior ofthe second vehicle comprises determining that the second vehicle changedlanes in response to the vehicle driving behavior of the first vehicle,and wherein applying the negative impact or the positive impact includesapplying a positive impact on the second vehicle or the driver of thesecond vehicle.
 7. The driving analysis computing device of claim 5,wherein determining the vehicle driving behavior of the first vehiclecomprises: determining that the first vehicle cut off the second vehiclebased on the first and second relative positions and the first andsecond times.
 8. The driving analysis computing device of claim 1,wherein the plurality of vehicle sensors within the first vehicleinclude at least one vehicle operation sensor configured to detect atleast one of: vehicle acceleration and vehicle braking.
 9. A method,comprising: determining, by a driving analysis device, vehicle drivingdata of a first vehicle using a plurality of vehicle sensors; receiving,by the driving analysis device, vehicle driving data of a second vehiclefrom the second vehicle using vehicle-to-vehicle communications;determining, by the driving analysis device, a vehicle driving behaviorof the first vehicle by performing an analysis of at least a firstportion of the first vehicle driving data; determining, by the drivinganalysis device, a vehicle driving behavior of the second vehicle, basedon an analysis of at least the second vehicle driving data; determining,by the driving analysis device, whether the vehicle driving behavior ofthe second vehicle was performed within a threshold amount of time ordistance of the vehicle driving behavior of the first vehicle; andapplying either a positive impact or a negative impact on the secondvehicle or a driver of the second vehicle in response to determiningthat the vehicle driving behavior of the second vehicle was performedwithin the threshold amount of time or distance of the vehicle drivingbehavior of the first vehicle.
 10. The method of claim 9, furthercomprising: determining a driver score for the first vehicle or thesecond vehicle, or a driver of the first or second vehicle, based on thecorresponding determined vehicle driving behavior.
 11. The method ofclaim 9, wherein the second vehicle driving data received from thesecond vehicle using vehicle-to-vehicle communication comprises at leastone of: a speed of the second vehicle; a position of the second vehicle;or a direction of travel of the second vehicle.
 12. The method of claim9, wherein the second vehicle driving data received from the secondvehicle using vehicle-to-vehicle communication comprises a driver scoreassociated with the second vehicle or the driver of the second vehicle,the driver score being generated based on a plurality of drivingbehaviors of the second vehicle or the driver of the second vehicle. 13.The method of claim 9, wherein determining the vehicle driving behaviorof the first vehicle comprises: determining a first relative position ofthe second vehicle with respect to the first vehicle at a first time;and determining a second relative position of the second vehicle withrespect to the first vehicle at a second time after the first time. 14.The method of claim 13, wherein determining the vehicle driving behaviorof the first vehicle comprises: determining that the first vehicletailgated, cut-off, or raced against the second vehicle, based on thefirst and second relative positions and the first and second times,wherein determining the vehicle driving behavior of the second vehiclecomprises determining that the second vehicle changed lanes in responseto the vehicle driving behavior of the first vehicle, and whereinapplying the negative impact or the positive impact includes applying apositive impact on the second vehicle or the driver of the secondvehicle.
 15. The method of claim 13, wherein determining the vehicledriving behavior of the second vehicle comprises: determining that thesecond vehicle followed the first vehicle at a safe following distance,yielded to the first vehicle, or defensively avoided the first vehicle,based on the first and second relative positions and the first andsecond times.
 16. A driving analysis computing device comprising: aprocessing unit comprising a processor; and memory storingcomputer-executable instructions, which when executed by the processingunit, cause the driving analysis computing device to: receive vehicledata transmitted by a plurality of second vehicles to a first vehicleusing vehicle-to-vehicle communications; determine whether a vehicledriving behavior of at least one of the second vehicles, determinedbased on the received vehicle data, was performed within a thresholdamount of time or distance of a vehicle driving behavior of the firstvehicle; and determine a driver score associated with the first vehicle,based on the vehicle data transmitted by the plurality of secondvehicles, including the vehicle driving behavior of the at least one ofthe second vehicles and the vehicle driving behavior of the firstvehicle.
 17. The driving analysis computing device of claim 16, whereinthe vehicle data transmitted by at least one of the second vehiclescomprises a driver score associated with the at least one of the secondvehicles or a driver of the at least one of the second vehicles.
 18. Thedriving analysis computing device of claim 16, wherein the vehicle datatransmitted by the at least one of the second vehicles comprises atleast one of: a speed of the at least one of the second vehicles; aposition of the at least one of the second vehicles; or a direction oftravel of the at least one of the second vehicles.